Oil Market Efficiency under a Machine Learning Perspective

Athanasia Dimitriadou, Periklis Gogas, Theophilos Papadimitriou and Vasilios Plakandaras
Additional contact information
Athanasia Dimitriadou: Department of Economics, Democritus University of Thrace, Komotini 69100, Greece

Forecasting, 2018, vol. 1, issue 1, 1-12

Abstract: Forecasting commodities and especially oil prices have attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market, attempting to forecast the West Texas Intermediate oil prices under a machine learning framework. In doing so, we compile a dataset of 38 potential explanatory variables that are often used in the relevant literature. Next, through a selection process, we build forecasting models that use past oil prices, refined oil products and exchange rates as independent variables. Our empirical findings suggest that the Support Vector Machines (SVM) model coupled with the non-linear Radial Basis Function kernel outperforms the linear SVM and the traditional logistic regression (LOGIT) models. Moreover, we provide evidence that points to the rejection of even the weak form of efficiency in the oil market.

Keywords: oil prices; forecasting; machine learning; support vector machines (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2571-9394/1/1/11/pdf (application/pdf)
https://www.mdpi.com/2571-9394/1/1/11/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jforec:v:1:y:2018:i:1:p:11-168:d:175388

Access Statistics for this article

Forecasting is currently edited by Ms. Joss Chen

More articles in Forecasting from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().